Biomedical Informatics

Overview

The achievement of an integrated understanding of biological function and interactions between multiple components of a biological system across hierarchical levels of analysis ranging from that of the gene to that of cell, tissue and organ is the next major frontier of biomedical science. Because of the inherent complexity of real biological systems, the development and analysis of computational models based directly on experimental data is necessary to achieve this understanding and translation to clinical practices.

An overarching goal of the computational biology and bioinformatics area of research emphasis is to develop the insights and the technology needed to gather the information needed to quantify biological function and to develop detailed predictive biomedical models. These will be used to accurately describe normal physiological function, to understand mechanisms that underlie pathological processes, to discover new biomarkers and therapeutical targets for diseases, and to evaluate medical and surgical interventions.

Transcript designation of computational biology and bioinformatics can include any of the following: 1) Training in the processing, analysis, mining, and integration of data from multiple sources such as genomic sequencing, epigenetics studies, gene and protein expression profiling, radiological studies, microscopy imagery, electrophysiological experiments, and numerical simulation studies, 2) Development of models to explain physiological and pathological processes, and 3) Development of database technology, data analysis techniques, high performance computing infrastructure, and systems software/middleware needed to carry out tasks associated with the aggregation and analysis of biomedical data, and needed to carry out biomedical simulations.

This area of research emphasis will involve the integration of large-scale data analysis, management, processing, and visualization with biomedical informatics. We will engage trainees in a highly collaborative interdisciplinary research program on high throughput data analysis and integration for biomedical applications in high end computing environment. We will also provide access to a customized program of course work in biomedical informatics, computer science and in basic and translational biomedical sciences.

Faculty members have diverse research interests which span analysis of gene sequence and gene and protein expression data, epigenetics, analysis and modeling of therapeutic and toxic drug effects, computational modeling of physiological systems, biomedical image processing and feature detection, visualization and development of database, data mining and data exploration algorithms and middleware.

Contact:
Qin Ma, PhD, Faculty Liaison
Courtney Hebert, MD, Faculty Liaison
Kin Fai Au, PhD, Faculty Liaison
Megan Gregory, PhD
Lijun Cheng, PhD

Program Curriculum

Students will ordinarily be expected to have strong backgrounds in computer science and/or undergraduate majors in computer science, engineering, physics, chemistry or biomedical engineering. However, candidates with weaker preparation in computer science and related fields may pursue this, but if warranted may be required by their dissertation advisory committee to take an additional customized program of coursework.

Year One

  • BSGP 7930 Laboratory Rotations (any semester)
  • BSGP 7000 Biomedical Sciences Concepts (Autumn)
  • BIOPHRM 7510 Professional and Ethical Issues in Biomedical Sciences (Spring)
  • BMI 5750 Methods in Biomedical Informatics and Data Science (Summer)
  • BMI 5780 Programming for Biomedical Informatics (Autumn)
  • BMI 5710 Introduction to Biomedical Informatics (Autumn)
  • BMI 5730 Introduction to Bioinformatics (Autumn or Spring) OR BMI 5740 Introduction to Research Informatics (Spring)

Year Two

  • BSGP 7070 Fundamentals of Grant Writing
  • BMI 7235 Machine Learning for Bioinformatics
  • Statistics Courses – Two Pathways
  • Students with little to no experience
    • STAT 5301 Intermediate Data Analysis I
    • STAT 5302 Intermediate Data Analysis II
  • Students with significant statistics experience
    • STAT 6450 Applied Regression Analysis
    • STAT 6570 Applied Bayesian Analysis

Year Two and Forward

Elective Options (10 credits total):

  • At least 6 credits should be from in-class courses, while the remaining can be seminars, etc.
  • In-class elective courses (at least 6 credits) need to be BMI 7000 or 8000-level courses.
  • BMI 7891 Seminars (4 credits) 

Course Requirements

In addition to the core curriculum, students must complete the following courses:

  • BMI 5710 Introduction to Biomedical Informatics (3 credits)
    • A survey of biomedical informatics theories and methods employed in the design, implementation and management of clinical information systems.
  • BMI 5730: Introduction to Bioinformatics (3 credits)
    • Introduces students to basic topics of bioinformatics including sequence analyses, proteomics, microarrays, regulatory networks, sequence and protein databases. Recommended background in molecular biology and computer science.
  • BMI 5750 (3 credits): Methods in Biomedical Informatics and Data Science
  • BMI 5780 (3 credits): Programming for Biomedical Informatics
  • BSGP 7000 (6 credits): Biomedical Sciences Survey
  • BMI 5710 (3 credits): Introduction to Biomedical Informatics
  • BIOPHRM 7510 (2 credits): Professional and Ethical Issues in Biomedical Science
  • BMI 5730 (3 credits): Introduction to Bioinformatics OR BMI 5740 (3 credits): Introduction to Research Informatics
  • BMI 7235 (3 credits): Applications of Machine Learning and Artificial Intelligence in Biomedical Informatics
  • BSGP 7070 (4 credits): Fundamentals of Grant Writing
  • STAT 5301 (4 credits): Intermediate Data Analysis OR
  • STAT 6450 (4 credits): Applied Regression Analysis
  • STAT 5302 (3 credits): Intermediate Data Analysis II OR STAT 6570 (2 credits): Applied Bayesian Analysis
  • BSGP 7930 (variable credits): Individual Studies in Biomedical Sciences
  • BSGP 7972 (1 credit): Research Seminar (Student Presentation) (taken the semester before or semester of graduation)
  • BSGP 8999 (variable credits): Research in Biomedical Sciences
  • Elective Courses (≥ 10 credits)
    • ≥ 6 of these credits must be from in-class BMI 7000- or 8000-level courses while the remaining 4 credits can be seminars. BMI 7891 Seminars are recommended for the remaining 4 credits.
  • Total credits: ≥ 80 credits

Students will also complete at least one of the following or its equivalent:

  • BMI 5720: Introduction to Imaging Informatics
  • BMI 5740: Introduction to Research Informatics
  • BMI 7830: Systems Biology
  • CSE 5441: Introduction to Parallel Computing
  • CSE 5431: Systems III: Introduction to Operating Systems (Converted from quarter course: CIS 660 Introduction to Operating Systems)
  • CSE 5433: Operating System Laboratory
  • CSE 5421: Introduction to Database Systems
  • CSE 5461: Computer Networking and Internet Technologies*
  • CSE 5331: Foundations II: Data Structures and Algorithms**
  • CIS 6441: Parallel Computing
  • CSE 5242: Advanced Database Management Systems
  • CSE 5542: Real-Time Rendering (Converted from quarter course: CIS 781 Introduction to 3D Image Generation)
  • CSE 5545: Advanced Computer Graphics (Converted from quarter course: CIS 782 Advanced 3D Image Generation)
  • CSE 6331: Algorithms
  • CSE 5243: Introduction to Data Mining
  • ECE 7868: Pattern Recognition and Machine Learning
  • ECE 7005: Information Theory
  • ECE 7866: Computer Vision
  • STAT 6410: Design and Analysis of Experiments
  • STAT 6450: Applied Regression Analysis
  • STAT 6560: Applied Multivariate Analysis
  • STAT 6570: Applied Bayesian Analysis
  • STAT 6625: Statistical Analysis of Genetic Data
  • STAT 7620: Elements of Statistical Learning

Credit requirements

  • The total credits >= 80 credits
  • Elective courses >= 10 credits
  • Among the 10 elective course credits, at least 6 should be from in-class courses, while the remaining can be seminars, etc.
  • In-class elective courses need to be BMI 7000 or 8000-level courses (see the attached “All BMI Courses.xlsx”). Any potential exemption or waiver need to be discussed with the BMI track Director and the advisor and approved.
  • The remaining 4 elective credits can be seminars. BMI 7891 Seminars are recommended for the remaining 4 credits.

Seminars

Due to the interdisciplinary nature of bioinformatics, relevant seminars are given through biomedical informatics, CIS, biomedical engineering, and biomedical sciences. To receive the designation of graduate specialization in biomedical informatics students will be required to register for a total of at least four credit hours (i.e., 4 semesters) in these relevant seminars approved by the student's advisor.

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